2eb17db210
Final two Python pipeline gaps ported to Go — InfluxDB live generation progress and DuckDB expansion pipeline status. Python scripts can now be archived. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
91 lines
3.5 KiB
Go
91 lines
3.5 KiB
Go
// Package ml provides ML inference, scoring, and training pipeline commands.
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//
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// Commands:
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// - core ml score: Score responses with heuristic and LLM judges
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// - core ml probe: Run capability and content probes against a model
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// - core ml export: Export golden set to training JSONL/Parquet
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// - core ml expand: Generate expansion responses
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// - core ml status: Show training and generation progress
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// - core ml gguf: Convert MLX LoRA adapter to GGUF format
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// - core ml convert: Convert MLX LoRA adapter to PEFT format
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// - core ml agent: Run the scoring agent daemon
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// - core ml worker: Run a distributed worker node
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// - core ml serve: Start OpenAI-compatible inference server
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// - core ml inventory: Show DuckDB table inventory with stats
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// - core ml query: Run ad-hoc SQL against DuckDB
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// - core ml metrics: Push golden set stats to InfluxDB
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// - core ml ingest: Ingest benchmark scores and training logs to InfluxDB
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// - core ml normalize: Deduplicate seeds into expansion prompts
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// - core ml seed-influx: Migrate golden set from DuckDB to InfluxDB
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// - core ml consolidate: Pull and merge response JSONL files from M3
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// - core ml import-all: Import all LEM data into DuckDB
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// - core ml approve: Filter scored expansions into training JSONL
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// - core ml publish: Upload Parquet dataset to HuggingFace Hub
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// - core ml coverage: Analyze seed coverage by region and domain
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// - core ml live: Show live generation progress from InfluxDB
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// - core ml expand-status: Show expansion pipeline progress
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package ml
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import (
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"forge.lthn.ai/core/go/pkg/cli"
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)
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func init() {
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cli.RegisterCommands(AddMLCommands)
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}
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var mlCmd = &cli.Command{
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Use: "ml",
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Short: "ML inference, scoring, and training pipeline",
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Long: "Commands for ML model scoring, probe evaluation, data export, and format conversion.",
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}
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// AddMLCommands registers the 'ml' command and all subcommands.
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func AddMLCommands(root *cli.Command) {
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initFlags()
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mlCmd.AddCommand(scoreCmd)
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mlCmd.AddCommand(probeCmd)
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mlCmd.AddCommand(exportCmd)
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mlCmd.AddCommand(expandCmd)
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mlCmd.AddCommand(statusCmd)
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mlCmd.AddCommand(ggufCmd)
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mlCmd.AddCommand(convertCmd)
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mlCmd.AddCommand(agentCmd)
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mlCmd.AddCommand(workerCmd)
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mlCmd.AddCommand(serveCmd)
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mlCmd.AddCommand(inventoryCmd)
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mlCmd.AddCommand(queryCmd)
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mlCmd.AddCommand(metricsCmd)
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mlCmd.AddCommand(ingestCmd)
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mlCmd.AddCommand(normalizeCmd)
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mlCmd.AddCommand(seedInfluxCmd)
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mlCmd.AddCommand(consolidateCmd)
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mlCmd.AddCommand(importCmd)
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mlCmd.AddCommand(approveCmd)
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mlCmd.AddCommand(publishCmd)
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mlCmd.AddCommand(coverageCmd)
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mlCmd.AddCommand(liveCmd)
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mlCmd.AddCommand(expandStatusCmd)
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root.AddCommand(mlCmd)
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}
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// Shared persistent flags.
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var (
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apiURL string
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judgeURL string
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judgeModel string
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influxURL string
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influxDB string
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dbPath string
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modelName string
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)
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func initFlags() {
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mlCmd.PersistentFlags().StringVar(&apiURL, "api-url", "http://10.69.69.108:8090", "OpenAI-compatible API URL")
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mlCmd.PersistentFlags().StringVar(&judgeURL, "judge-url", "http://10.69.69.108:11434", "Judge model API URL (Ollama)")
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mlCmd.PersistentFlags().StringVar(&judgeModel, "judge-model", "gemma3:27b", "Judge model name")
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mlCmd.PersistentFlags().StringVar(&influxURL, "influx", "", "InfluxDB URL (default http://10.69.69.165:8181)")
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mlCmd.PersistentFlags().StringVar(&influxDB, "influx-db", "", "InfluxDB database (default training)")
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mlCmd.PersistentFlags().StringVar(&dbPath, "db", "", "DuckDB database path (or set LEM_DB env)")
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mlCmd.PersistentFlags().StringVar(&modelName, "model", "", "Model name for API")
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}
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